SPSS -Quantitative – Data Analysis

SPSS -Quantitative – Data Analysis
Project description
1 Description and aims (Using IBM SPSS Statistic 20)
The aim is to carry out a number of data analysis tasks specified below using SPSS on the datasets provided. You should present a summary of your analysis, including key statistical results, their interpretation, your conclusions and recommendations. Only refer to important SPSS printouts in the text, additional and SPSS printouts that are not absolutely necessary for anybody other than experts, should be provided in an appendix.

Each of the tasks should be composed of three aspects:
1) Management aspect. You are supposed to set the problem, provide a summary of key findings, your conclusions and recommendations that arise from your findings. This should be written in a non-technical way, such that your line manager, who has not studied statistics, can understand your point.

2) Technical aspect. This is where you present a technical summary of your analysis, explaining all your steps and methodologies used, and provide the key statistical results. Here you can include summary tables compiled by yourself, if you find them helpful, but do not reproduce the computer output, unless absolutely necessary.

3) Appendix. The appendix should contain a reasonable number of printouts of some (only the most relevant) of the computer output generated in SPSS that you refer to in the other two sections but does not fit in the other sections because it would distract from the overall story that you are telling.
If you are reproducing SPSS printouts excessively, without clear connection to the management and/or technical aspects reported in the coursework, this will have negative knock-on effects on the mark. Coursework should be 5-6 pages not more.

Remember that quality is more important than quantity!
2. Coursework tasks

— data analysis (intermediate)
Use hsb2.sav (see attachment) to do the following exercises (NB — You should start with presenting and summarising the variables and then moving onto the statistical model to examine the relationships between variables.)
1.1.1 Multiple regression:
a) Fit a regression equation relating Y= science (science score) to math (math score), socst (social studies score), read (reading score) and female (Xs). Report the prediction equation and output.
b) Interpret the estimated regression coefficients.
c) Find the predicted science score for a male who scored 50 in math and 60 in reading and social studies.
d) Test the null hypothesis that female has no effect on the response, controlling for the other predictors. Interpret.
e) Construct a 95% confidence interval describing the true effect of female, controlling for the other predictors. Interpret.

1.1.2 Logistic regression:
a. Generate a dichotomous variable, honcomp (for honours composition), using write (writing score).
b. honcomp = 1 if writing score is greater than or equal to 60 honcomp = 0 if writing score is less than 60
c. Use logistic regression to model the effect of read, science and ses on the probability of being in honours composition (honcomp). Report the prediction equation and interpret the model fit.
d. Interpret the effects of the explanatory variables on the odds of being in honours composition.